AI Software Engineer (Vehicle Engineering)
Core
Developing core AI technologies to accelerate engineering for launch vehicles, spacecrafts, and satellite constellations by training internal models and building agentic tools.
Role type
Senior IC AI Software Engineer (LLM/ML Systems)
Builds
Production-grade AI tools, agentic workflows, and foundation models for engineering design, testing, and mission operations.
Domain
Aerospace / Propulsion & Avionics / Large Language Models
Deliverable
production ML models | product features
Required skills
Python, LLM transformer architectures, model training and fine-tuning, reinforcement learning, computer vision, MLOps, statistics, Linux, Git, Docker, Kubernetes
Preferred skills
PyTorch, TensorFlow, JAX, distributed training on GPU clusters, hyperparameter optimization, experiment tracking, MLflow, Weights & Biases, vector databases, PostgreSQL, relational and non-relational databases, feature stores
Technologies
PyTorch, TensorFlow, JAX, MLflow, Weights & Biases, Docker, Kubernetes, PostgreSQL, vector databases
Responsibilities
Design and train highly reliable, scalable AI/ML models; Design agentic AI systems and multi-agent workflows; Build and optimize large-scale machine learning training pipelines; Develop and fine-tune foundation models (LLMs, vision models, multimodal systems); Create production-grade AI tools for data analysis, anomaly detection, predictive modeling, and automated decision-making; Collaborate with peers on AI architecture, model design, training strategies, and code reviews; Rapidly build and iterate on AI prototypes, rigorously quantifying model performance, accuracy, and technical constraints; Own the complete AI model lifecycle — from data preparation and training infrastructure to deployment, monitoring, and continuous improvement; Deep-dive into complex engineering problems to identify and implement efficient, custom-trained AI solutions; Establish rigorous AI standards for model validation, safety, reliability, bias mitigation, and data security; Ensure all AI systems undergo thorough testing and validation to deliver accurate, trustworthy, and production-ready outputs
